Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations7597
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory172.0 B

Variable types

Text3
Numeric12
Categorical6
DateTime1

Alerts

Bathroom is highly overall correlated with Bedroom2 and 1 other fieldsHigh correlation
Bedroom2 is highly overall correlated with Bathroom and 3 other fieldsHigh correlation
Category is highly overall correlated with Price and 2 other fieldsHigh correlation
CouncilArea is highly overall correlated with Lattitude and 3 other fieldsHigh correlation
Landsize is highly overall correlated with Bedroom2 and 1 other fieldsHigh correlation
Lattitude is highly overall correlated with CouncilArea and 1 other fieldsHigh correlation
Longtitude is highly overall correlated with CouncilArea and 2 other fieldsHigh correlation
Postcode is highly overall correlated with CouncilArea and 3 other fieldsHigh correlation
Price is highly overall correlated with Bedroom2 and 3 other fieldsHigh correlation
Price_per_Room is highly overall correlated with Category and 1 other fieldsHigh correlation
Regionname is highly overall correlated with CouncilArea and 2 other fieldsHigh correlation
Rooms is highly overall correlated with Bathroom and 3 other fieldsHigh correlation
Type is highly overall correlated with CategoryHigh correlation
Landsize is highly skewed (γ1 = 36.21793171) Skewed
Car has 587 (7.7%) zeros Zeros
Landsize has 1453 (19.1%) zeros Zeros

Reproduction

Analysis started2025-02-23 13:14:30.685157
Analysis finished2025-02-23 13:15:05.304631
Duration34.62 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Suburb
Text

Distinct142
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size59.5 KiB
2025-02-23T13:15:05.595875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length15
Mean length9.7321311
Min length3

Characters and Unicode

Total characters73935
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAbbotsford
2nd rowAbbotsford
3rd rowAbbotsford
4th rowAbbotsford
5th rowAbbotsford
ValueCountFrequency (%)
east 674
 
6.5%
north 426
 
4.1%
bentleigh 314
 
3.0%
west 313
 
3.0%
south 270
 
2.6%
melbourne 269
 
2.6%
brunswick 263
 
2.5%
brighton 252
 
2.4%
reservoir 227
 
2.2%
vale 202
 
1.9%
Other values (112) 7205
69.2%
2025-02-23T13:15:06.130900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6637
 
9.0%
o 6338
 
8.6%
r 6140
 
8.3%
n 5354
 
7.2%
t 4801
 
6.5%
a 4612
 
6.2%
l 3994
 
5.4%
i 3736
 
5.1%
s 3649
 
4.9%
2818
 
3.8%
Other values (37) 25856
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73935
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6637
 
9.0%
o 6338
 
8.6%
r 6140
 
8.3%
n 5354
 
7.2%
t 4801
 
6.5%
a 4612
 
6.2%
l 3994
 
5.4%
i 3736
 
5.1%
s 3649
 
4.9%
2818
 
3.8%
Other values (37) 25856
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73935
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6637
 
9.0%
o 6338
 
8.6%
r 6140
 
8.3%
n 5354
 
7.2%
t 4801
 
6.5%
a 4612
 
6.2%
l 3994
 
5.4%
i 3736
 
5.1%
s 3649
 
4.9%
2818
 
3.8%
Other values (37) 25856
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73935
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6637
 
9.0%
o 6338
 
8.6%
r 6140
 
8.3%
n 5354
 
7.2%
t 4801
 
6.5%
a 4612
 
6.2%
l 3994
 
5.4%
i 3736
 
5.1%
s 3649
 
4.9%
2818
 
3.8%
Other values (37) 25856
35.0%
Distinct7516
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size59.5 KiB
2025-02-23T13:15:06.548632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length21
Mean length13.61906
Min length8

Characters and Unicode

Total characters103464
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7439 ?
Unique (%)97.9%

Sample

1st row85 Turner St
2nd row25 Bloomburg St
3rd row5 Charles St
4th row40 Federation La
5th row55a Park St
ValueCountFrequency (%)
st 5002
 
21.9%
rd 1676
 
7.3%
ct 215
 
0.9%
gr 182
 
0.8%
dr 137
 
0.6%
4 134
 
0.6%
5 127
 
0.6%
pde 126
 
0.6%
3 123
 
0.5%
6 109
 
0.5%
Other values (4740) 15056
65.8%
2025-02-23T13:15:07.154664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15290
 
14.8%
t 7545
 
7.3%
S 5533
 
5.3%
e 5363
 
5.2%
r 4633
 
4.5%
a 4422
 
4.3%
1 4035
 
3.9%
n 4032
 
3.9%
o 3850
 
3.7%
l 3430
 
3.3%
Other values (54) 45331
43.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 103464
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15290
 
14.8%
t 7545
 
7.3%
S 5533
 
5.3%
e 5363
 
5.2%
r 4633
 
4.5%
a 4422
 
4.3%
1 4035
 
3.9%
n 4032
 
3.9%
o 3850
 
3.7%
l 3430
 
3.3%
Other values (54) 45331
43.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 103464
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15290
 
14.8%
t 7545
 
7.3%
S 5533
 
5.3%
e 5363
 
5.2%
r 4633
 
4.5%
a 4422
 
4.3%
1 4035
 
3.9%
n 4032
 
3.9%
o 3850
 
3.7%
l 3430
 
3.3%
Other values (54) 45331
43.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 103464
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15290
 
14.8%
t 7545
 
7.3%
S 5533
 
5.3%
e 5363
 
5.2%
r 4633
 
4.5%
a 4422
 
4.3%
1 4035
 
3.9%
n 4032
 
3.9%
o 3850
 
3.7%
l 3430
 
3.3%
Other values (54) 45331
43.8%

Rooms
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8427011
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.5 KiB
2025-02-23T13:15:07.265562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.95715397
Coefficient of variation (CV)0.33670581
Kurtosis0.73758351
Mean2.8427011
Median Absolute Deviation (MAD)1
Skewness0.44878548
Sum21596
Variance0.91614373
MonotonicityNot monotonic
2025-02-23T13:15:07.400127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 3126
41.1%
2 2368
31.2%
4 1323
17.4%
1 442
 
5.8%
5 297
 
3.9%
6 30
 
0.4%
8 6
 
0.1%
7 5
 
0.1%
ValueCountFrequency (%)
1 442
 
5.8%
2 2368
31.2%
3 3126
41.1%
4 1323
17.4%
5 297
 
3.9%
6 30
 
0.4%
7 5
 
0.1%
8 6
 
0.1%
ValueCountFrequency (%)
8 6
 
0.1%
7 5
 
0.1%
6 30
 
0.4%
5 297
 
3.9%
4 1323
17.4%
3 3126
41.1%
2 2368
31.2%
1 442
 
5.8%

Type
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.5 KiB
h
4981 
u
1955 
t
661 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7597
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowh
2nd rowh
3rd rowh
4th rowh
5th rowh

Common Values

ValueCountFrequency (%)
h 4981
65.6%
u 1955
 
25.7%
t 661
 
8.7%

Length

2025-02-23T13:15:07.577327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T13:15:07.692748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
h 4981
65.6%
u 1955
 
25.7%
t 661
 
8.7%

Most occurring characters

ValueCountFrequency (%)
h 4981
65.6%
u 1955
 
25.7%
t 661
 
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7597
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
h 4981
65.6%
u 1955
 
25.7%
t 661
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7597
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
h 4981
65.6%
u 1955
 
25.7%
t 661
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7597
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
h 4981
65.6%
u 1955
 
25.7%
t 661
 
8.7%

Price
Real number (ℝ)

High correlation 

Distinct1711
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1088322.6
Minimum85000
Maximum6500000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.5 KiB
2025-02-23T13:15:07.856409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum85000
5-th percentile394600
Q1645000
median910000
Q31350000
95-th percentile2346000
Maximum6500000
Range6415000
Interquartile range (IQR)705000

Descriptive statistics

Standard deviation656081.06
Coefficient of variation (CV)0.60283692
Kurtosis6.4732471
Mean1088322.6
Median Absolute Deviation (MAD)325000
Skewness1.9858169
Sum8.267987 × 109
Variance4.3044236 × 1011
MonotonicityNot monotonic
2025-02-23T13:15:08.077365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600000 65
 
0.9%
1100000 62
 
0.8%
1300000 59
 
0.8%
650000 57
 
0.8%
800000 54
 
0.7%
1000000 54
 
0.7%
700000 51
 
0.7%
1200000 50
 
0.7%
900000 49
 
0.6%
1400000 47
 
0.6%
Other values (1701) 7049
92.8%
ValueCountFrequency (%)
85000 1
 
< 0.1%
131000 1
 
< 0.1%
145000 2
< 0.1%
185000 1
 
< 0.1%
190000 1
 
< 0.1%
200000 1
 
< 0.1%
210000 3
< 0.1%
215000 1
 
< 0.1%
216000 2
< 0.1%
220000 1
 
< 0.1%
ValueCountFrequency (%)
6500000 1
< 0.1%
6250000 1
< 0.1%
5800000 1
< 0.1%
5700000 1
< 0.1%
5525000 1
< 0.1%
5500000 2
< 0.1%
5100000 1
< 0.1%
5050000 1
< 0.1%
5046000 1
< 0.1%
4850000 1
< 0.1%

Method
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size59.5 KiB
S
5034 
PI
940 
SP
923 
VB
675 
SA
 
25

Length

Max length2
Median length1
Mean length1.33737
Min length1

Characters and Unicode

Total characters10160
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowSP
4th rowPI
5th rowVB

Common Values

ValueCountFrequency (%)
S 5034
66.3%
PI 940
 
12.4%
SP 923
 
12.1%
VB 675
 
8.9%
SA 25
 
0.3%

Length

2025-02-23T13:15:08.260935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T13:15:08.382698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
s 5034
66.3%
pi 940
 
12.4%
sp 923
 
12.1%
vb 675
 
8.9%
sa 25
 
0.3%

Most occurring characters

ValueCountFrequency (%)
S 5982
58.9%
P 1863
 
18.3%
I 940
 
9.3%
V 675
 
6.6%
B 675
 
6.6%
A 25
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10160
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 5982
58.9%
P 1863
 
18.3%
I 940
 
9.3%
V 675
 
6.6%
B 675
 
6.6%
A 25
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10160
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 5982
58.9%
P 1863
 
18.3%
I 940
 
9.3%
V 675
 
6.6%
B 675
 
6.6%
A 25
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10160
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 5982
58.9%
P 1863
 
18.3%
I 940
 
9.3%
V 675
 
6.6%
B 675
 
6.6%
A 25
 
0.2%
Distinct184
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size59.5 KiB
2025-02-23T13:15:08.715349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length23
Median length19
Mean length6.5199421
Min length1

Characters and Unicode

Total characters49532
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)0.7%

Sample

1st rowBiggin
2nd rowBiggin
3rd rowBiggin
4th rowBiggin
5th rowNelson
ValueCountFrequency (%)
nelson 1008
 
13.3%
jellis 812
 
10.7%
hockingstuart 706
 
9.3%
barry 504
 
6.6%
marshall 439
 
5.8%
buxton 361
 
4.8%
ray 265
 
3.5%
biggin 245
 
3.2%
brad 224
 
2.9%
woodards 182
 
2.4%
Other values (172) 2851
37.5%
2025-02-23T13:15:09.233386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 4765
 
9.6%
s 4179
 
8.4%
a 4011
 
8.1%
e 3877
 
7.8%
r 3559
 
7.2%
o 3321
 
6.7%
n 3137
 
6.3%
i 2885
 
5.8%
t 2271
 
4.6%
g 1687
 
3.4%
Other values (43) 15840
32.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49532
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 4765
 
9.6%
s 4179
 
8.4%
a 4011
 
8.1%
e 3877
 
7.8%
r 3559
 
7.2%
o 3321
 
6.7%
n 3137
 
6.3%
i 2885
 
5.8%
t 2271
 
4.6%
g 1687
 
3.4%
Other values (43) 15840
32.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49532
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 4765
 
9.6%
s 4179
 
8.4%
a 4011
 
8.1%
e 3877
 
7.8%
r 3559
 
7.2%
o 3321
 
6.7%
n 3137
 
6.3%
i 2885
 
5.8%
t 2271
 
4.6%
g 1687
 
3.4%
Other values (43) 15840
32.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49532
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 4765
 
9.6%
s 4179
 
8.4%
a 4011
 
8.1%
e 3877
 
7.8%
r 3559
 
7.2%
o 3321
 
6.7%
n 3137
 
6.3%
i 2885
 
5.8%
t 2271
 
4.6%
g 1687
 
3.4%
Other values (43) 15840
32.0%

Date
Date

Distinct42
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size59.5 KiB
Minimum2016-01-28 00:00:00
Maximum2017-05-20 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-23T13:15:09.375713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:15:09.572593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)

Distance
Real number (ℝ)

Distinct93
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5851915
Minimum1.2
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.5 KiB
2025-02-23T13:15:09.761886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile2.6
Q15.9
median8.8
Q311.2
95-th percentile13.9
Maximum15
Range13.8
Interquartile range (IQR)5.3

Descriptive statistics

Standard deviation3.5893563
Coefficient of variation (CV)0.41808692
Kurtosis-1.0131369
Mean8.5851915
Median Absolute Deviation (MAD)2.7
Skewness-0.14617508
Sum65221.7
Variance12.883479
MonotonicityNot monotonic
2025-02-23T13:15:09.967813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.2 589
 
7.8%
9.2 338
 
4.4%
13.9 305
 
4.0%
7.8 226
 
3.0%
13 223
 
2.9%
2.6 208
 
2.7%
8 204
 
2.7%
3.3 194
 
2.6%
4.6 184
 
2.4%
5.9 168
 
2.2%
Other values (83) 4958
65.3%
ValueCountFrequency (%)
1.2 29
 
0.4%
1.5 14
 
0.2%
1.6 78
 
1.0%
1.8 35
 
0.5%
1.9 20
 
0.3%
2.1 51
 
0.7%
2.3 51
 
0.7%
2.5 58
 
0.8%
2.6 208
2.7%
2.8 32
 
0.4%
ValueCountFrequency (%)
15 31
 
0.4%
14.9 31
 
0.4%
14.7 32
 
0.4%
14.6 50
 
0.7%
14.5 41
 
0.5%
14 27
 
0.4%
13.9 305
4.0%
13.8 131
1.7%
13.7 85
 
1.1%
13.6 34
 
0.4%

Postcode
Real number (ℝ)

High correlation 

Distinct94
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3096.3282
Minimum3000
Maximum3207
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.5 KiB
2025-02-23T13:15:10.184855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3000
5-th percentile3013
Q13044
median3081
Q33146
95-th percentile3204
Maximum3207
Range207
Interquartile range (IQR)102

Descriptive statistics

Standard deviation60.313405
Coefficient of variation (CV)0.019479009
Kurtosis-1.1959414
Mean3096.3282
Median Absolute Deviation (MAD)47
Skewness0.27549294
Sum23522805
Variance3637.7068
MonotonicityNot monotonic
2025-02-23T13:15:10.389166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3073 227
 
3.0%
3121 210
 
2.8%
3020 209
 
2.8%
3165 204
 
2.7%
3040 186
 
2.4%
3032 177
 
2.3%
3204 165
 
2.2%
3046 164
 
2.2%
3058 164
 
2.2%
3163 160
 
2.1%
Other values (84) 5731
75.4%
ValueCountFrequency (%)
3000 32
 
0.4%
3002 14
 
0.2%
3003 17
 
0.2%
3006 29
 
0.4%
3008 3
 
< 0.1%
3011 133
1.8%
3012 143
1.9%
3013 102
1.3%
3015 122
1.6%
3016 78
1.0%
ValueCountFrequency (%)
3207 104
1.4%
3206 72
0.9%
3205 51
 
0.7%
3204 165
2.2%
3189 50
 
0.7%
3188 101
1.3%
3187 108
1.4%
3186 144
1.9%
3185 55
 
0.7%
3184 85
1.1%

Bedroom2
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8029485
Minimum0
Maximum20
Zeros11
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size59.5 KiB
2025-02-23T13:15:10.545685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum20
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.96829777
Coefficient of variation (CV)0.34545685
Kurtosis13.981085
Mean2.8029485
Median Absolute Deviation (MAD)1
Skewness1.1808598
Sum21294
Variance0.93760057
MonotonicityNot monotonic
2025-02-23T13:15:10.692880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
3 3144
41.4%
2 2459
32.4%
4 1234
 
16.2%
1 452
 
5.9%
5 260
 
3.4%
6 25
 
0.3%
0 11
 
0.1%
7 5
 
0.1%
8 3
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
0 11
 
0.1%
1 452
 
5.9%
2 2459
32.4%
3 3144
41.4%
4 1234
 
16.2%
5 260
 
3.4%
6 25
 
0.3%
7 5
 
0.1%
8 3
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
9 3
 
< 0.1%
8 3
 
< 0.1%
7 5
 
0.1%
6 25
 
0.3%
5 260
 
3.4%
4 1234
 
16.2%
3 3144
41.4%
2 2459
32.4%
1 452
 
5.9%

Bathroom
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5038831
Minimum0
Maximum8
Zeros27
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size59.5 KiB
2025-02-23T13:15:10.830987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.69633202
Coefficient of variation (CV)0.4630227
Kurtosis4.2223773
Mean1.5038831
Median Absolute Deviation (MAD)0
Skewness1.5142174
Sum11425
Variance0.48487828
MonotonicityNot monotonic
2025-02-23T13:15:10.964266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 4417
58.1%
2 2565
33.8%
3 507
 
6.7%
4 59
 
0.8%
0 27
 
0.4%
5 15
 
0.2%
6 4
 
0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 27
 
0.4%
1 4417
58.1%
2 2565
33.8%
3 507
 
6.7%
4 59
 
0.8%
5 15
 
0.2%
6 4
 
0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 2
 
< 0.1%
6 4
 
0.1%
5 15
 
0.2%
4 59
 
0.8%
3 507
 
6.7%
2 2565
33.8%
1 4417
58.1%
0 27
 
0.4%

Car
Real number (ℝ)

Zeros 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5298144
Minimum0
Maximum8
Zeros587
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size59.5 KiB
2025-02-23T13:15:11.114326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.91908678
Coefficient of variation (CV)0.6007832
Kurtosis4.4556033
Mean1.5298144
Median Absolute Deviation (MAD)1
Skewness1.3278195
Sum11622
Variance0.84472052
MonotonicityNot monotonic
2025-02-23T13:15:11.261791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 3489
45.9%
2 2843
37.4%
0 587
 
7.7%
3 369
 
4.9%
4 252
 
3.3%
5 25
 
0.3%
6 22
 
0.3%
8 5
 
0.1%
7 5
 
0.1%
ValueCountFrequency (%)
0 587
 
7.7%
1 3489
45.9%
2 2843
37.4%
3 369
 
4.9%
4 252
 
3.3%
5 25
 
0.3%
6 22
 
0.3%
7 5
 
0.1%
8 5
 
0.1%
ValueCountFrequency (%)
8 5
 
0.1%
7 5
 
0.1%
6 22
 
0.3%
5 25
 
0.3%
4 252
 
3.3%
3 369
 
4.9%
2 2843
37.4%
1 3489
45.9%
0 587
 
7.7%

Landsize
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct1158
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean454.12926
Minimum0
Maximum75100
Zeros1453
Zeros (%)19.1%
Negative0
Negative (%)0.0%
Memory size59.5 KiB
2025-02-23T13:15:11.447315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1132
median339
Q3616
95-th percentile911.2
Maximum75100
Range75100
Interquartile range (IQR)484

Descriptive statistics

Standard deviation1274.3774
Coefficient of variation (CV)2.8061997
Kurtosis1804.8884
Mean454.12926
Median Absolute Deviation (MAD)252
Skewness36.217932
Sum3450020
Variance1624037.7
MonotonicityNot monotonic
2025-02-23T13:15:11.670511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1453
 
19.1%
650 46
 
0.6%
697 25
 
0.3%
700 24
 
0.3%
217 21
 
0.3%
696 21
 
0.3%
586 20
 
0.3%
597 20
 
0.3%
590 19
 
0.3%
215 19
 
0.3%
Other values (1148) 5929
78.0%
ValueCountFrequency (%)
0 1453
19.1%
1 1
 
< 0.1%
14 1
 
< 0.1%
29 1
 
< 0.1%
36 2
 
< 0.1%
41 1
 
< 0.1%
43 1
 
< 0.1%
47 1
 
< 0.1%
49 1
 
< 0.1%
50 2
 
< 0.1%
ValueCountFrequency (%)
75100 1
< 0.1%
41400 1
< 0.1%
37000 1
< 0.1%
21700 1
< 0.1%
17200 2
< 0.1%
15900 1
< 0.1%
15100 1
< 0.1%
14500 1
< 0.1%
10100 1
< 0.1%
8680 1
< 0.1%

CouncilArea
Categorical

High correlation 

Distinct20
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size59.5 KiB
Boroondara
813 
Moreland
771 
Moonee Valley
701 
Darebin
651 
Glen Eira
647 
Other values (15)
4014 

Length

Max length13
Median length10
Mean length9.2353561
Min length4

Characters and Unicode

Total characters70161
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowYarra
2nd rowYarra
3rd rowYarra
4th rowYarra
5th rowYarra

Common Values

ValueCountFrequency (%)
Boroondara 813
10.7%
Moreland 771
10.1%
Moonee Valley 701
9.2%
Darebin 651
 
8.6%
Glen Eira 647
 
8.5%
Stonnington 521
 
6.9%
Maribyrnong 484
 
6.4%
Yarra 455
 
6.0%
Port Phillip 452
 
5.9%
Banyule 361
 
4.8%
Other values (10) 1741
22.9%

Length

2025-02-23T13:15:11.880936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
boroondara 813
 
8.4%
moreland 771
 
8.0%
moonee 701
 
7.2%
valley 701
 
7.2%
darebin 651
 
6.7%
glen 647
 
6.7%
eira 647
 
6.7%
stonnington 521
 
5.4%
maribyrnong 484
 
5.0%
yarra 455
 
4.7%
Other values (14) 3294
34.0%

Most occurring characters

ValueCountFrequency (%)
n 8656
12.3%
o 7839
11.2%
a 7523
10.7%
r 6734
 
9.6%
e 5835
 
8.3%
l 4409
 
6.3%
i 4184
 
6.0%
M 2599
 
3.7%
y 2181
 
3.1%
2088
 
3.0%
Other values (21) 18113
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70161
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 8656
12.3%
o 7839
11.2%
a 7523
10.7%
r 6734
 
9.6%
e 5835
 
8.3%
l 4409
 
6.3%
i 4184
 
6.0%
M 2599
 
3.7%
y 2181
 
3.1%
2088
 
3.0%
Other values (21) 18113
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70161
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 8656
12.3%
o 7839
11.2%
a 7523
10.7%
r 6734
 
9.6%
e 5835
 
8.3%
l 4409
 
6.3%
i 4184
 
6.0%
M 2599
 
3.7%
y 2181
 
3.1%
2088
 
3.0%
Other values (21) 18113
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70161
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 8656
12.3%
o 7839
11.2%
a 7523
10.7%
r 6734
 
9.6%
e 5835
 
8.3%
l 4409
 
6.3%
i 4184
 
6.0%
M 2599
 
3.7%
y 2181
 
3.1%
2088
 
3.0%
Other values (21) 18113
25.8%

Lattitude
Real number (ℝ)

High correlation 

Distinct2288
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-37.810194
Minimum-37.9462
Maximum-37.6783
Zeros0
Zeros (%)0.0%
Negative7597
Negative (%)100.0%
Memory size59.5 KiB
2025-02-23T13:15:12.063105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-37.9462
5-th percentile-37.9225
Q1-37.8536
median-37.8037
Q3-37.763
95-th percentile-37.71556
Maximum-37.6783
Range0.2679
Interquartile range (IQR)0.0906

Descriptive statistics

Standard deviation0.061722454
Coefficient of variation (CV)-0.0016324289
Kurtosis-0.75374846
Mean-37.810194
Median Absolute Deviation (MAD)0.0449
Skewness-0.26934708
Sum-287244.04
Variance0.0038096614
MonotonicityNot monotonic
2025-02-23T13:15:12.299427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-37.8361 18
 
0.2%
-37.8424 14
 
0.2%
-37.7969 13
 
0.2%
-37.7923 12
 
0.2%
-37.8198 12
 
0.2%
-37.7634 11
 
0.1%
-37.7642 11
 
0.1%
-37.822 11
 
0.1%
-37.7813 11
 
0.1%
-37.7988 11
 
0.1%
Other values (2278) 7473
98.4%
ValueCountFrequency (%)
-37.9462 1
 
< 0.1%
-37.946 1
 
< 0.1%
-37.9454 1
 
< 0.1%
-37.9452 1
 
< 0.1%
-37.9449 3
< 0.1%
-37.9448 1
 
< 0.1%
-37.9446 2
< 0.1%
-37.9444 1
 
< 0.1%
-37.9443 2
< 0.1%
-37.9442 2
< 0.1%
ValueCountFrequency (%)
-37.6783 1
< 0.1%
-37.6804 1
< 0.1%
-37.6859 1
< 0.1%
-37.6862 1
< 0.1%
-37.6864 1
< 0.1%
-37.6866 1
< 0.1%
-37.687 1
< 0.1%
-37.6876 1
< 0.1%
-37.6878 1
< 0.1%
-37.6885 1
< 0.1%

Longtitude
Real number (ℝ)

High correlation 

Distinct2699
Distinct (%)35.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.98706
Minimum144.7889
Maximum145.1438
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.5 KiB
2025-02-23T13:15:12.515296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum144.7889
5-th percentile144.85806
Q1144.9325
median144.996
Q3145.0454
95-th percentile145.0995
Maximum145.1438
Range0.3549
Interquartile range (IQR)0.1129

Descriptive statistics

Standard deviation0.074722736
Coefficient of variation (CV)0.00051537519
Kurtosis-0.66464596
Mean144.98706
Median Absolute Deviation (MAD)0.0542
Skewness-0.3010697
Sum1101466.7
Variance0.0055834873
MonotonicityNot monotonic
2025-02-23T13:15:12.724867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
144.9966 14
 
0.2%
144.985 13
 
0.2%
145.0001 11
 
0.1%
145.0043 11
 
0.1%
145.021 11
 
0.1%
144.9886 11
 
0.1%
145.0243 11
 
0.1%
144.997 11
 
0.1%
145.0505 10
 
0.1%
144.9999 10
 
0.1%
Other values (2689) 7484
98.5%
ValueCountFrequency (%)
144.7889 1
< 0.1%
144.7918 1
< 0.1%
144.7925 1
< 0.1%
144.7928 1
< 0.1%
144.7929 1
< 0.1%
144.7936 1
< 0.1%
144.7964 1
< 0.1%
144.7975 1
< 0.1%
144.7988 1
< 0.1%
144.7991 1
< 0.1%
ValueCountFrequency (%)
145.1438 1
< 0.1%
145.1436 1
< 0.1%
145.1417 1
< 0.1%
145.1392 1
< 0.1%
145.139 1
< 0.1%
145.1386 1
< 0.1%
145.1384 1
< 0.1%
145.138 1
< 0.1%
145.1379 1
< 0.1%
145.1375 1
< 0.1%

Regionname
Categorical

High correlation 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size59.5 KiB
Southern Metropolitan
3109 
Northern Metropolitan
2206 
Western Metropolitan
1686 
Eastern Metropolitan
564 
South-Eastern Metropolitan
 
32

Length

Max length26
Median length21
Mean length20.724891
Min length20

Characters and Unicode

Total characters157447
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorthern Metropolitan
2nd rowNorthern Metropolitan
3rd rowNorthern Metropolitan
4th rowNorthern Metropolitan
5th rowNorthern Metropolitan

Common Values

ValueCountFrequency (%)
Southern Metropolitan 3109
40.9%
Northern Metropolitan 2206
29.0%
Western Metropolitan 1686
22.2%
Eastern Metropolitan 564
 
7.4%
South-Eastern Metropolitan 32
 
0.4%

Length

2025-02-23T13:15:12.924619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T13:15:13.057695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
metropolitan 7597
50.0%
southern 3109
20.5%
northern 2206
 
14.5%
western 1686
 
11.1%
eastern 564
 
3.7%
south-eastern 32
 
0.2%

Most occurring characters

ValueCountFrequency (%)
t 22823
14.5%
o 20541
13.0%
r 17400
11.1%
e 16880
10.7%
n 15194
9.7%
a 8193
 
5.2%
M 7597
 
4.8%
i 7597
 
4.8%
7597
 
4.8%
p 7597
 
4.8%
Other values (9) 26028
16.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 157447
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 22823
14.5%
o 20541
13.0%
r 17400
11.1%
e 16880
10.7%
n 15194
9.7%
a 8193
 
5.2%
M 7597
 
4.8%
i 7597
 
4.8%
7597
 
4.8%
p 7597
 
4.8%
Other values (9) 26028
16.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 157447
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 22823
14.5%
o 20541
13.0%
r 17400
11.1%
e 16880
10.7%
n 15194
9.7%
a 8193
 
5.2%
M 7597
 
4.8%
i 7597
 
4.8%
7597
 
4.8%
p 7597
 
4.8%
Other values (9) 26028
16.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 157447
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 22823
14.5%
o 20541
13.0%
r 17400
11.1%
e 16880
10.7%
n 15194
9.7%
a 8193
 
5.2%
M 7597
 
4.8%
i 7597
 
4.8%
7597
 
4.8%
p 7597
 
4.8%
Other values (9) 26028
16.5%

Propertycount
Real number (ℝ)

Distinct141
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7460.303
Minimum389
Maximum21650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.5 KiB
2025-02-23T13:15:13.261153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum389
5-th percentile2211
Q14480
median6567
Q310331
95-th percentile14949
Maximum21650
Range21261
Interquartile range (IQR)5851

Descriptive statistics

Standard deviation4371.9393
Coefficient of variation (CV)0.58602704
Kurtosis1.4766184
Mean7460.303
Median Absolute Deviation (MAD)2694
Skewness1.1317704
Sum56675922
Variance19113853
MonotonicityNot monotonic
2025-02-23T13:15:13.465475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21650 227
 
3.0%
10969 204
 
2.7%
14949 185
 
2.4%
8870 177
 
2.3%
14577 153
 
2.0%
10579 144
 
1.9%
11918 143
 
1.9%
14887 141
 
1.9%
9264 136
 
1.8%
11204 125
 
1.6%
Other values (131) 5962
78.5%
ValueCountFrequency (%)
389 5
 
0.1%
394 1
 
< 0.1%
438 5
 
0.1%
534 5
 
0.1%
588 19
0.3%
608 5
 
0.1%
790 11
0.1%
802 10
0.1%
821 7
 
0.1%
851 11
0.1%
ValueCountFrequency (%)
21650 227
3.0%
17496 32
 
0.4%
14949 185
2.4%
14887 141
1.9%
14577 153
2.0%
13240 120
1.6%
11918 143
1.9%
11364 123
1.6%
11308 114
1.5%
11204 125
1.6%

Price_per_Room
Real number (ℝ)

High correlation 

Distinct2034
Distinct (%)26.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean383553.06
Minimum32750
Maximum2450000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.5 KiB
2025-02-23T13:15:13.796431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum32750
5-th percentile185000
Q1260000
median343333.33
Q3458333.33
95-th percentile710900
Maximum2450000
Range2417250
Interquartile range (IQR)198333.33

Descriptive statistics

Standard deviation180360.77
Coefficient of variation (CV)0.47023683
Kurtosis12.492406
Mean383553.06
Median Absolute Deviation (MAD)94583.333
Skewness2.3159361
Sum2.9138526 × 109
Variance3.2530009 × 1010
MonotonicityNot monotonic
2025-02-23T13:15:14.239313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300000 80
 
1.1%
400000 72
 
0.9%
350000 64
 
0.8%
500000 61
 
0.8%
250000 58
 
0.8%
325000 56
 
0.7%
450000 56
 
0.7%
200000 54
 
0.7%
260000 50
 
0.7%
275000 48
 
0.6%
Other values (2024) 6998
92.1%
ValueCountFrequency (%)
32750 1
< 0.1%
36250 1
< 0.1%
85000 1
< 0.1%
86666.66667 1
< 0.1%
95000 1
< 0.1%
96250 1
< 0.1%
100333.3333 1
< 0.1%
101666.6667 1
< 0.1%
106666.6667 1
< 0.1%
110000 1
< 0.1%
ValueCountFrequency (%)
2450000 1
< 0.1%
2400000 1
< 0.1%
2262500 1
< 0.1%
2115000 1
< 0.1%
2083333.333 1
< 0.1%
1812500 1
< 0.1%
1720000 1
< 0.1%
1700000 1
< 0.1%
1675000 1
< 0.1%
1630000 1
< 0.1%

Year_sold
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.5 KiB
2016
6336 
2017
1261 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters30388
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2017
4th row2017
5th row2016

Common Values

ValueCountFrequency (%)
2016 6336
83.4%
2017 1261
 
16.6%

Length

2025-02-23T13:15:14.657833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T13:15:14.775074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2016 6336
83.4%
2017 1261
 
16.6%

Most occurring characters

ValueCountFrequency (%)
2 7597
25.0%
0 7597
25.0%
1 7597
25.0%
6 6336
20.9%
7 1261
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30388
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 7597
25.0%
0 7597
25.0%
1 7597
25.0%
6 6336
20.9%
7 1261
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30388
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 7597
25.0%
0 7597
25.0%
1 7597
25.0%
6 6336
20.9%
7 1261
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30388
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 7597
25.0%
0 7597
25.0%
1 7597
25.0%
6 6336
20.9%
7 1261
 
4.1%

Category
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.5 KiB
Affordable
3823 
Expensive
3774 

Length

Max length10
Median length10
Mean length9.503225
Min length9

Characters and Unicode

Total characters72196
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowExpensive
2nd rowExpensive
3rd rowExpensive
4th rowAffordable
5th rowExpensive

Common Values

ValueCountFrequency (%)
Affordable 3823
50.3%
Expensive 3774
49.7%

Length

2025-02-23T13:15:15.016907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-23T13:15:15.234165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
affordable 3823
50.3%
expensive 3774
49.7%

Most occurring characters

ValueCountFrequency (%)
e 11371
15.8%
f 7646
 
10.6%
A 3823
 
5.3%
o 3823
 
5.3%
r 3823
 
5.3%
d 3823
 
5.3%
a 3823
 
5.3%
b 3823
 
5.3%
l 3823
 
5.3%
E 3774
 
5.2%
Other values (6) 22644
31.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72196
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 11371
15.8%
f 7646
 
10.6%
A 3823
 
5.3%
o 3823
 
5.3%
r 3823
 
5.3%
d 3823
 
5.3%
a 3823
 
5.3%
b 3823
 
5.3%
l 3823
 
5.3%
E 3774
 
5.2%
Other values (6) 22644
31.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72196
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 11371
15.8%
f 7646
 
10.6%
A 3823
 
5.3%
o 3823
 
5.3%
r 3823
 
5.3%
d 3823
 
5.3%
a 3823
 
5.3%
b 3823
 
5.3%
l 3823
 
5.3%
E 3774
 
5.2%
Other values (6) 22644
31.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72196
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 11371
15.8%
f 7646
 
10.6%
A 3823
 
5.3%
o 3823
 
5.3%
r 3823
 
5.3%
d 3823
 
5.3%
a 3823
 
5.3%
b 3823
 
5.3%
l 3823
 
5.3%
E 3774
 
5.2%
Other values (6) 22644
31.4%

Interactions

2025-02-23T13:15:00.462183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:14:32.537916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-23T13:14:38.956749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-23T13:14:48.611973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-23T13:14:43.620024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-23T13:15:04.103656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:14:36.146229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:14:38.419476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:14:40.497175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:14:42.925524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-23T13:14:50.781375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:14:53.437435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:14:55.592423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:14:57.795933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:14:59.837604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:15:04.322439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:14:36.416686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:14:38.603066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:14:40.681898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:14:43.106488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:14:45.334429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:14:48.037867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:14:51.433941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-23T13:14:55.761354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:14:57.972932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:15:00.038610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:15:04.491269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:14:36.656294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:14:38.788920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:14:40.863808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:14:43.269146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:14:45.507295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:14:48.303607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:14:51.608797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:14:53.784535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:14:55.919993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:14:58.139369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-23T13:15:00.214538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-23T13:15:15.439539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
BathroomBedroom2CarCategoryCouncilAreaDistanceLandsizeLattitudeLongtitudeMethodPostcodePricePrice_per_RoomPropertycountRegionnameRoomsTypeYear_sold
Bathroom1.0000.5610.3560.3850.0890.1020.213-0.0980.1320.0700.1040.4820.162-0.0420.0860.5730.2330.000
Bedroom20.5611.0000.4500.4460.1470.3060.5070.0530.0940.065-0.0170.598-0.004-0.1020.0860.9330.4020.000
Car0.3560.4501.0000.3380.1330.3210.4180.0090.1180.0310.0240.3290.051-0.0550.0850.4510.2960.062
Category0.3850.4460.3381.0000.3530.1700.0000.2900.2700.1140.3000.7680.5920.2050.2420.5000.5200.049
CouncilArea0.0890.1470.1330.3531.0000.4790.0000.5520.6350.1060.7440.1790.1890.4330.8480.1440.2220.065
Distance0.1020.3060.3210.1700.4791.0000.397-0.0200.2480.0580.120-0.045-0.329-0.2340.3010.3000.1830.000
Landsize0.2130.5070.4180.0000.0000.3971.0000.0880.1440.000-0.0070.4320.128-0.1100.0000.5230.0300.049
Lattitude-0.0980.0530.0090.2900.552-0.0200.0881.000-0.3390.053-0.645-0.262-0.392-0.0400.4510.0480.1470.046
Longtitude0.1320.0940.1180.2700.6350.2480.144-0.3391.0000.0740.6880.2720.2780.1100.6000.0930.1590.017
Method0.0700.0650.0310.1140.1060.0580.0000.0530.0741.0000.0810.0830.0550.0540.0800.0910.0750.024
Postcode0.104-0.0170.0240.3000.7440.120-0.007-0.6450.6880.0811.0000.2670.3770.1790.649-0.0230.1470.045
Price0.4820.5980.3290.7680.179-0.0450.432-0.2620.2720.0830.2671.0000.7290.0100.1740.6230.4230.056
Price_per_Room0.162-0.0040.0510.5920.189-0.3290.128-0.3920.2780.0550.3770.7291.0000.1200.198-0.0200.1690.060
Propertycount-0.042-0.102-0.0550.2050.433-0.234-0.110-0.0400.1100.0540.1790.0100.1201.0000.358-0.1050.1390.034
Regionname0.0860.0860.0850.2420.8480.3010.0000.4510.6000.0800.6490.1740.1980.3581.0000.1020.1230.031
Rooms0.5730.9330.4510.5000.1440.3000.5230.0480.0930.091-0.0230.623-0.020-0.1050.1021.0000.4540.000
Type0.2330.4020.2960.5200.2220.1830.0300.1470.1590.0750.1470.4230.1690.1390.1230.4541.0000.011
Year_sold0.0000.0000.0620.0490.0650.0000.0490.0460.0170.0240.0450.0560.0600.0340.0310.0000.0111.000

Missing values

2025-02-23T13:15:04.819471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-23T13:15:05.123641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SuburbAddressRoomsTypePriceMethodSellerGDateDistancePostcodeBedroom2BathroomCarLandsizeCouncilAreaLattitudeLongtitudeRegionnamePropertycountPrice_per_RoomYear_soldCategory
0Abbotsford85 Turner St2h1480000SBiggin2016-12-0323067211202Yarra-38145Northern Metropolitan40197400002016Expensive
1Abbotsford25 Bloomburg St2h1035000SBiggin2016-02-0423067210156Yarra-38145Northern Metropolitan40195175002016Expensive
2Abbotsford5 Charles St3h1465000SPBiggin2017-03-0423067320134Yarra-38145Northern Metropolitan40194883332017Expensive
3Abbotsford40 Federation La3h850000PIBiggin2017-03-042306732194Yarra-38145Northern Metropolitan40192833332017Affordable
4Abbotsford55a Park St4h1600000VBNelson2016-06-0423067312120Yarra-38145Northern Metropolitan40194000002016Expensive
5Abbotsford129 Charles St2h941000SJellis2016-05-0723067210181Yarra-38145Northern Metropolitan40194705002016Expensive
6Abbotsford124 Yarra St3h1876000SNelson2016-05-0723067420245Yarra-38145Northern Metropolitan40196253332016Expensive
7Abbotsford98 Charles St2h1636000SNelson2016-10-0823067212256Yarra-38145Northern Metropolitan40198180002016Expensive
8Abbotsford6/241 Nicholson St1u300000SBiggin2016-10-08230671110Yarra-38145Northern Metropolitan40193000002016Affordable
9Abbotsford10 Valiant St2h1097000SBiggin2016-10-0823067312220Yarra-38145Northern Metropolitan40195485002016Expensive
SuburbAddressRoomsTypePriceMethodSellerGDateDistancePostcodeBedroom2BathroomCarLandsizeCouncilAreaLattitudeLongtitudeRegionnamePropertycountPrice_per_RoomYear_soldCategory
7587Brighton East7 Holmhurst Ct4t1700000PIHodges2017-04-29113187432312Bayside-38145Southern Metropolitan69384250002017Expensive
7588Brighton East55 Milroy St5h1900000PIHodges2017-04-29113187532583Bayside-38145Southern Metropolitan69383800002017Expensive
7589Brighton East7/36 Union St3t972000SGary2017-04-29113187321119Bayside-38145Southern Metropolitan69383240002017Expensive
7590Brunswick129 Glenlyon Rd3h1652000SJellis2017-05-0653056312506Moreland-38145Northern Metropolitan119185506672017Expensive
7591Brunswick3/15 Mitchell St2u735000SPWalshe2017-05-0653056212173Moreland-38145Northern Metropolitan119183675002017Affordable
7592Brunswick217 Victoria St3h1210000SWoodards2017-05-0653056310282Moreland-38145Northern Metropolitan119184033332017Expensive
7593Brunswick4/287 Albion St1h412000SNelson2017-04-085305611185Moreland-38145Northern Metropolitan119184120002017Affordable
7594Brunswick2/199 Barkly St1u528000SNelson2017-04-08530561110Moreland-38145Northern Metropolitan119185280002017Affordable
7595Brunswick10 Bennie St3h1115000SCaine2017-04-0853056311249Moreland-38145Northern Metropolitan119183716672017Expensive
7596Brunswick373 Brunswick Rd3h1300000VBNelson2017-04-0853056323299More-38145Southern Metropolitan65674333332017Expensive